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A goodness-of-fit test for progressively first-failure-censored data from a proportional hazard rate model | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 01 مرداد 1405 اصل مقاله (289.96 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.88330.1076 | ||
| نویسندگان | ||
| Mohammad Vali Ahmadi* ؛ Hamid Reza Moheghi | ||
| University of Bojnord, Bojnord, Iran | ||
| چکیده | ||
| Progressive first-failure censoring schemes are potentially useful in practical applications where budget constraints exist or rapid testing is required. Moreover, several common sampling schemes such as first-failure censoring, progressive Type-II censoring, Type-II censoring, and complete sampling can be viewed as special cases of the progressive first-failure censoring scheme. In this article, we propose a goodness-of-fit test statistic to assess whether a progressively first-failure-censored sample originates from a distribution belonging to the proportional hazard rate model. This model encompasses several well-known lifetime distributions, including the exponential, Rayleigh, Lomax, Burr Type-XII, Weibull, Gompertz, and Pareto distributions, among others. We derive the null distribution of the proposed test statistic. Through Monte Carlo simulations, we evaluate the power of the proposed test under a range of different alternative distributions, assuming the null distribution to be exponential, Rayleigh, or Pareto. Finally, we present several numerical real-world examples to demonstrate the practical applicability of the proposed goodness-of-fit test. We also summarize the main findings and provide concluding remarks. | ||
| کلیدواژهها | ||
| Goodness-of-fit test؛ Proportional hazard rate model؛ Progressive first-failure censoring؛ Monte Carlo simulation؛ Power study | ||
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آمار تعداد مشاهده مقاله: 3 تعداد دریافت فایل اصل مقاله: 9 |
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